AN INTELLIGENT SENTIMENT ANALYSIS SYSTEM FOR MEASURING CUSTOMER LOYALTY AND MAKING DECISIONS BASED ON FUZZY LOGIC
Abstract
This paper presents an intelligent approach of measuring customer loyalty to a specific
product based on the analysis of comments. General sentiment analysis in tweets and messages is
quite common, but task-oriented analysis of user opinions and measuring their level of loyalty is a
new idea. The tricky part of doing task-oriented sentiment analysis lies in measuring customer
loyalty to a particular product based on how customers feel about that product itself. The resulting
data on the level of customer loyalty to the product can help a new customer to make a decision on
a specific product, taking into account its various characteristics and feedback from previous customers. The dataset was a large dataset of online customer testimonials from Amazon.com. The set of
initial data is a set of reviews, from which the proposed approach forms an aggregated assessment of
opinions, then a fuzzy logic model is used to measure customer loyalty to the product. In the proposed
approach, the input text is first processed using such methods as tokenization, removal of stop words,
lemmatization, then the parts of speech are marked and the polarity of the reviews is analyzed, then
fuzzy logic methods are applied to the obtained aggregated estimates to determine the degree of customer
loyalty to the product. This work used various open API libraries such as SentiWordNet, Stanford
CoreNLP, etc. The approach used focuses on identifying the sentiment of reviews, which can be
positive, negative and neutral. In our study, we used a triangular membership function, also known
as trimf, because it supports three variables and creates a relationship between them. The implementation
of the approach ensures high accuracy in determining loyalty to e-commerce products, which
is superior to previous approaches, and the use of fuzzy logic has significantly increased the values of
such indicators as precision, recall, and F-measure.
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